🧑‍🔬 Guangyao Dou

🏛️ Affiliations

🎓Johns Hopkins UniversityBaltimore · United States2025–2026 · 2 papersEducation since 2025 · PhD Student, Computer Sciencecurrent🎓University of PennsylvaniaPhiladelphia · United States2023–2025 · 8 papersEducation since 2023🎓Brandeis UniversityWaltham · United States2019–2023 · 3 papersEducation 2019–2023 · Bachelor of Science, Computer Science

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Towards Safer Large Language Models through Machine Unlearning (2024 · 72 citations)
   Large language models can still give dangerous answers to harmful prompts. Earlier unlearning methods based on gradient ascent suppress such answers but tend to damage replies to ordinary requests. The authors propose Selective Knowledge negation Unlearning (SKU), a two-stage framework that removes harmful knowledge…
2. Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench (2025 · 24 citations)
   Large generative models trained on web-scale data can memorize personal details and later reveal them. Machine unlearning has been studied for text-only LLMs, but multimodal models (MLLMs) are harder: knowledge about a person is spread across images and text, so removing it from one modality may leave it recoverable…
3. Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models (2025 · 12 citations)
   Multimodal large language models can memorize private information, and erasing it is harder than in text-only LLMs.

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Towards Safer Large Language Models through Machine UnlearningFindings of the Association for Computational LinguisticsZheyuan Liu, Guangyao Dou, Zhaoxuan Tan, et al.2024 · 72 citations
2. Protecting Privacy in Multimodal Large Language Models with MLLMU-BenchConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Zheyuan Liu, Guangyao Dou, Mengzhao Jia, et al.2025 · 24 citations
3. Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zheyuan Liu, Guangyao Dou, Xiangchi Yuan, et al.2025 · 12 citations
4. DeonticBench: A Benchmark for Reasoning over Rules ↗arXiv:2604.04443 · no summary yetGuangyao Dou et al.2026 · 1 citation
5. Inference-Time Control for Trustworthy Large Language Models ↗Preprints.org · no summary yetYuyang Bai et al.2026
6. DAR: Deontic Reasoning with Agentic Harnesses ↗arXiv:2606.05009 · no summary yetGuangyao Dou et al.2026

🤝 Co-authors here

8 co-authors with a page, the most papers together first.

• Meng Jiang ×3
• Zhaoxuan Tan ×3
• Zheyuan Liu ×3
• Chunhui Zhang
• Mengzhao Jia
• Qingkai Zeng
• Xiangchi Yuan
• Yijun Tian

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2025
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2024
• Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2025
• Preprints.org

🔗 Cited by

Papers of our library cite their works 78 times (of the 149,439 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated 30 October 2026

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